More than 90 million Americans live with chronic diseases. Care for these Americans accounts for more than 60% of the nation's medical care costs. By definition, a chronic disease progresses over time with a generally predictable set of costly exacerbations, complications and recurrences.
A central precept to the discussions on health care costs is that there is a cost-quality function from which one may derive a linear cost-quality curve. On such a cost quality curve, so the argument goes, any reduction in the planned budgetary growth of health care dollars will result in lower-quality health care. To the contrary, however, the actual cost-quality curve for health care has been shown to be significantly non-linear. FIGS. 1A and 1B depict the perceived and actual cost-quality curves showing the relationship between cost and health care quality. FIG. 1A depicts an expected cost-quality curve 10, while FIG. 1B depicts the actual non-linear cost-quality curve 12.
In the actual health care cost-quality curve 12 of FIG. 1B, increased costs do not always correlate to improved quality. Instead, there has been shown to be a “quality valley” 14, where health care quality actually decreases 16 with increased expenditures for health care. Understanding this potential “quality valley” 14 is essential to the creation of real improvements and cost savings in health care. That is, if “quality valley” 14 could be either carefully managed against for either its elimination or, if it cannot be eliminated, its avoidance, there could be an opportunity simultaneously decrease costs and improve quality.
Research for two common medical diagnoses, congestive heart failure (CHF) and pneumonia, for example, indicates a wide variation in outcomes among providers. By matching severity-adjusted mortality data to hospital-specific charge data, one can observe that higher average charges often associate with a lower quality of care.
These results support the conclusion that significant variation in charges exists among hospitals. These variances may imply that higher costs associate with lower quality (resulting, for example, in higher severity-adjusted mortality rates). This represents unnecessary resource utilization.
Making comparisons among the ten countries having the highest Gross Domestic Product (GDP) per capita further validates this conclusion. Data from the United States Statistical Abstract indicates that the United States spends the largest percentage of its gross domestic product (GDP) on health care, while exhibiting one of the world's lowest life expectancy at birth (LEAB rates). International health expenditure studies are difficult to conduct, however, because of factors such as data quality, variable accounting methods, and significant social-cultural differences. Despite these shortcomings, a highly reasonable conclusion remains that, with the present systems and methods for managing diseases such as CHF and pneumonia, spending more dollars on health care results in a decrease in health care quality received, as measured on a large scale, for example, by LEAB rates.
Although every physician should consider the best interests of his/her patients, the medical system has evolved with a history of incentives, threats (e.g., medical malpractice), and customs that can significantly increase costs, while not improving quality.
Additionally, disease intervention processes and treatments, all too frequently seek to improve patient comfort, longevity, and physical functioning. These processes and treatments employ surrogate endpoints based on logical, but unproven, extensions of an existing, but incomplete, disease process model. A great number of physician actions are based on these surrogate endpoints. These surrogate endpoints, however, often lead to increased costs and examinations without improved results.
A need exists, therefore, for significant efforts to optimize the cost and quality relationship of healthcare. Prior efforts focus on the development of “best practices” protocols, medical error reduction, bulk purchasing and pharmaceutical benefits management, new medicine, minimally invasive surgery, and the redesign of care systems. These efforts seek to more effectively manage demand for health services. While past practices are important, these efforts fail to address any way to reduce costs and improve quality in healthcare. In particular, they already fail to provide for complication identification and proactive symptom treatment of chronic disease exacerbation in the individual patient.
One avenue of attempting to better practice early complication identification and proactive symptom treatment has been through the use of computers. Such attempts to use computers, for example, seek to automate more routine aspects of medical processes and treatments. These computerized schemes, for example, may center on communicating automatically with a patient regarding a previously diagnosed disease. In such processes, automatic therapy adjustment becomes responsive to information received from the patient. Such automated schemes of medical treatment typically involve the use of computers and the Internet to treat patients remotely. The purpose of these conventional schemes of remote treatment by using computers or Internet avoids unnecessary office visits, thereby effecting savings in overall healthcare costs. Thereby, a physician may be virtually “present” at the patient's location and help treat the patient remotely.
Unfortunately, attempts to automate patient-physician communications do not change previous paradigms for certain chronic diseases. With many of these chronic diseases, infrequent physician visits, either in person or through a virtual office, are accepted as normal. Thus, it has not been possible to identify evolving complications, exacerbations or recurrences, within certain classes of chronic disease patients. At the same time, early interventions may mitigate a patient's worsening clinical condition. In fact, in many instances, early interventions may avoid the need for emergency medical services altogether. Also, disease predictive models have not proven effective to predict the worsening of a patient's condition from chronic diseases. Because of these and other reasons, a standardized therapy based upon broad demographic models is difficult or impossible to employ remotely.
A need exists, therefore, for a system and method that allow early detection of chronic disease exacerbations or complications in order to decrease the need for emergency medical services while measurably improving patient outcomes.
Returning to the above discussion regarding the health care cost-quality curve, often chronic diseases, such as CHF, exhibit a non-linear cost-quality relationship. Accordingly, managing a patient's condition preventively, as opposed to remedially, may assist in avoiding a “quality valley.” That is, such preventive management could avoid the situation of increased health care expenditures, ironically, resulting in lower returns in patient outcome. If it were possible to achieve early detection of chronic disease exacerbations or complications, well before the greater cost treatments are necessary, then the health care industry could avoid troubling regions of a non-linear cost-quality curve. In a larger sense, therefore, there is a need for an early detection method and system making it possible to greatly reduce overall health care costs while improving patient quality of life.